ArticleHealth science reports2023
Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study.
Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- From Molecular Pathways to Artificial Intelligence: Advancing the Understanding and Management of Osteosarcopenia.International journal of molecular sciences · 2026Review
- Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.Frontiers in endocrinology · 2026Article
- Optimised knowledge distillation for efficient social media emotion recognition using DistilBERT and ALBERT.Scientific reports · 2025Article
- Depression detection with machine learning of structural and non-structural dual languages.Healthcare technology letters · 2024Article
- Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.Healthcare technology letters · 2024Article
- Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study.Health science reports · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine-learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010-2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K-nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self-Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.
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Registered trials
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